





"""simple docstring"""





import uuid
from typing import Any, Dict, List, Optional, Union

from ..utils import add_end_docstrings, is_tf_available, is_torch_available, logging
from .base import PIPELINE_INIT_ARGS, Pipeline


if is_tf_available():
       import tensorflow as tf

if is_torch_available():
       import torch


SCREAMING_SNAKE_CASE__   =  logging.get_logger(__name__)

class 						lowercase   :





      def __init__(			self ,		lowercase = None ,		lowercase = None ,		lowercase=None ,		lowercase=None					)						->	str:
            if not conversation_id:
                  lowerCAmelCase    		=			uuid.uuida()
            if past_user_inputs is None:
                  lowerCAmelCase    		=			[]
            if generated_responses is None:
                  lowerCAmelCase    		=			[]

            lowerCAmelCase    		=			conversation_id
            lowerCAmelCase    		=			past_user_inputs
            lowerCAmelCase    		=			generated_responses
            lowerCAmelCase    		=			text





      def __eq__(			self ,		lowercase					)						->	Any:
            if not isinstance(lowercase ,		lowercase					):
                  return False
            if self.uuid == other.uuid:
                  return True
            return (
                self.new_user_input == other.new_user_input
                and self.past_user_inputs == other.past_user_inputs
                and self.generated_responses == other.generated_responses
            )





      def        _snake_case     (			self ,		lowercase ,		lowercase = False					)						->	List[Any]:
            if self.new_user_input:
                  if overwrite:
                        logger.warning(
                            f'User input added while unprocessed input was existing: "{self.new_user_input}" was overwritten '
                            f'with: "{text}".'					)
                        lowerCAmelCase    		=			text
                  else:
                        logger.warning(
                            f'User input added while unprocessed input was existing: "{self.new_user_input}" new input '
                            f'ignored: "{text}". Set `overwrite` to True to overwrite unprocessed user input'					)
            else:
                  lowerCAmelCase    		=			text





      def        _snake_case     (			self					)						->	Union[str, Any]:
            if self.new_user_input:
                  self.past_user_inputs.append(self.new_user_input					)
            lowerCAmelCase    		=			None





      def        _snake_case     (			self ,		lowercase					)						->	Any:
            self.generated_responses.append(lowercase					)





      def        _snake_case     (			self					)						->	Optional[Any]:
            for user_input, generated_response in zip(self.past_user_inputs ,		self.generated_responses					):
                  yield True, user_input
                  yield False, generated_response
            if self.new_user_input:
                  yield True, self.new_user_input





      def __repr__(			self					)						->	List[Any]:
            lowerCAmelCase    		=			f'Conversation id: {self.uuid} \n'
            for is_user, text in self.iter_texts():
                  lowerCAmelCase    		=			"""user""" if is_user else """bot"""
                  output += f'{name} >> {text} \n'
            return output







@add_end_docstrings(
    _UpperCAmelCase	,    r'\n        min_length_for_response (`int`, *optional*, defaults to 32):\n            The minimum length (in number of tokens) for a response.\n        minimum_tokens (`int`, *optional*, defaults to 10):\n            The minimum length of tokens to leave for a response.\n    '	,    )
class 						lowercase   (   _UpperCAmelCase   ):





      def __init__(			self ,		*lowercase ,		**lowercase					)						->	List[Any]:
            super().__init__(*lowercase ,		**lowercase					)
            if self.tokenizer.pad_token_id is None:
                  lowerCAmelCase    		=			self.tokenizer.eos_token





      def        _snake_case     (			self ,		lowercase=None ,		lowercase=None ,		lowercase=None ,		**lowercase					)						->	str:
            lowerCAmelCase    		=			{}
            lowerCAmelCase    		=			{}
            lowerCAmelCase    		=			{}

            if min_length_for_response is not None:
                  lowerCAmelCase    		=			min_length_for_response
            if minimum_tokens is not None:
                  lowerCAmelCase    		=			minimum_tokens

            if "max_length" in generate_kwargs:
                  lowerCAmelCase    		=			generate_kwargs["""max_length"""]
                  # self.max_length = generate_kwargs.get("max_length", self.model.config.max_length)
            if clean_up_tokenization_spaces is not None:
                  lowerCAmelCase    		=			clean_up_tokenization_spaces

            if generate_kwargs:
                  forward_params.update(lowercase					)
            return preprocess_params, forward_params, postprocess_params





      def __call__(			self ,		lowercase ,		lowercase=0 ,		**lowercase					)						->	Dict:
            lowerCAmelCase    		=			super().__call__(lowercase ,		num_workers=lowercase ,		**lowercase					)
            if isinstance(lowercase ,		lowercase					) and len(lowercase					) == 1:
                  return outputs[0]
            return outputs





      def        _snake_case     (			self ,		lowercase ,		lowercase=32					)						->	Dict[str, Any]:
            if not isinstance(lowercase ,		lowercase					):
                  raise ValueError("""ConversationalPipeline, expects Conversation as inputs"""					)
            if conversation.new_user_input is None:
                  raise ValueError(
                      f'Conversation with UUID {type(conversation.uuid					)} does not contain new user input to process. '
                      """Add user inputs with the conversation's `add_user_input` method"""					)
            if hasattr(self.tokenizer ,		"""_build_conversation_input_ids"""					):
                  lowerCAmelCase    		=			self.tokenizer._build_conversation_input_ids(lowercase					)
            else:
                  # If the tokenizer cannot handle conversations, we default to only the old version
                  lowerCAmelCase    		=			self._legacy_parse_and_tokenize(lowercase					)

            if self.framework == "pt":
                  lowerCAmelCase    		=			torch.LongTensor([input_ids]					)
            elif self.framework == "tf":
                  lowerCAmelCase    		=			tf.constant([input_ids]					)
            return {"input_ids": input_ids, "conversation": conversation}





      def        _snake_case     (			self ,		lowercase ,		lowercase=10 ,		**lowercase					)						->	List[str]:
            lowerCAmelCase    		=			generate_kwargs.get("""max_length""" ,		self.model.config.max_length					)

            lowerCAmelCase    		=			model_inputs["""input_ids"""].shape[1]
            if max_length - minimum_tokens < n:
                  logger.warning(f'Conversation input is to long ({n}), trimming it to ({max_length} - {minimum_tokens})'					)
                  lowerCAmelCase    		=			max_length - minimum_tokens
                  lowerCAmelCase    		=			model_inputs["""input_ids"""][:, -trim:]
                  if "attention_mask" in model_inputs:
                        lowerCAmelCase    		=			model_inputs["""attention_mask"""][:, -trim:]
            lowerCAmelCase    		=			model_inputs.pop("""conversation"""					)
            lowerCAmelCase    		=			max_length
            lowerCAmelCase    		=			self.model.generate(**lowercase ,		**lowercase					)
            if self.model.config.is_encoder_decoder:
                  lowerCAmelCase    		=			1
            else:
                  lowerCAmelCase    		=			n
            return {"output_ids": output_ids[:, start_position:], "conversation": conversation}





      def        _snake_case     (			self ,		lowercase ,		lowercase=True					)						->	List[Any]:
            lowerCAmelCase    		=			model_outputs["""output_ids"""]
            lowerCAmelCase    		=			self.tokenizer.decode(
                output_ids[0] ,		skip_special_tokens=lowercase ,		clean_up_tokenization_spaces=lowercase ,		)
            lowerCAmelCase    		=			model_outputs["""conversation"""]
            conversation.mark_processed()
            conversation.append_response(lowercase					)
            return conversation





      def        _snake_case     (			self ,		lowercase					)						->	Dict:
            lowerCAmelCase    		=			self.tokenizer.eos_token_id
            lowerCAmelCase    		=			[]
            for is_user, text in conversation.iter_texts():
                  if eos_token_id is not None:
                        input_ids.extend(self.tokenizer.encode(lowercase ,		add_special_tokens=lowercase					) + [eos_token_id]					)
                  else:
                        input_ids.extend(self.tokenizer.encode(lowercase ,		add_special_tokens=lowercase					)					)

            if len(lowercase					) > self.tokenizer.model_max_length:
                  lowerCAmelCase    		=			input_ids[-self.tokenizer.model_max_length :]
            return input_ids













"""simple docstring"""





import logging
import os

from .state import PartialState

class 						lowercase   (   logging.LoggerAdapter   ):





      @staticmethod
      def        _snake_case     (			lowercase					)						->	Optional[Any]:
            lowerCAmelCase    		=			PartialState()
            return not main_process_only or (main_process_only and state.is_main_process)





      def        _snake_case     (			self ,		lowercase ,		lowercase ,		*lowercase ,		**lowercase					)						->	Dict:
            if PartialState._shared_state == {}:
                  raise RuntimeError(
                      """You must initialize the accelerate state by calling either `PartialState()` or `Accelerator()` before using the logging utility."""					)
            lowerCAmelCase    		=			kwargs.pop("""main_process_only""" ,		lowercase					)
            lowerCAmelCase    		=			kwargs.pop("""in_order""" ,		lowercase					)

            if self.isEnabledFor(lowercase					):
                  if self._should_log(lowercase					):
                        lowerCAmelCase		,						lowerCAmelCase    		=			self.process(lowercase ,		lowercase					)
                        self.logger.log(lowercase ,		lowercase ,		*lowercase ,		**lowercase					)

                  elif in_order:
                        lowerCAmelCase    		=			PartialState()
                        for i in range(state.num_processes					):
                              if i == state.process_index:
                                    lowerCAmelCase		,						lowerCAmelCase    		=			self.process(lowercase ,		lowercase					)
                                    self.logger.log(lowercase ,		lowercase ,		*lowercase ,		**lowercase					)
                              state.wait_for_everyone()







def      UpperCAmelCase__				(    SCREAMING_SNAKE_CASE	:		str				,		SCREAMING_SNAKE_CASE	:		str = None     ):







      '''simple docstring'''




      if log_level is None:
            lowerCAmelCase    		=			os.environ.get("""ACCELERATE_LOG_LEVEL"""				,		SCREAMING_SNAKE_CASE     )
      lowerCAmelCase    		=			logging.getLogger(SCREAMING_SNAKE_CASE     )
      if log_level is not None:
            logger.setLevel(log_level.upper()     )
            logger.root.setLevel(log_level.upper()     )
      return MultiProcessAdapter(SCREAMING_SNAKE_CASE				,		{}     )






